Tim Hansen

• Mapping challenging confined underwater environments pushes the boundaries of what is possible for state-of-the-art robotics. Current state-of-the-art high-performance equipment allows already for accurate mapping in such scenarios. However, these systems are often expensive. Affordable underwater robotic systems and sensors come with significantly reduced capabilities. Especially sonars are necessary for mapping unknown environments, due to cluttered water resulting in bad visibility for vision based sensors. Yet, affordable sonar sensors suffer from higher noise levels, reduced accuracy, and limited coverage. Consequently, developing methods to achieve reliable and accurate mapping of challenging environments with affordable hardware remains an open research question. This thesis presents a Fourier-SOFT in 2D (FS2D) registration method for robust matching of high-noise 2D sonar scans. A Simultaneous Localization and Mapping (SLAM) framework designed to the unique challenges of affordable Mechanical Scanning Sonars (MSS) is presented, integrating this FS2D registration method. In the context of the digitization of cultural heritage, the Bunker Valentin Memorial in Bremen is surveyed, and maps of its multiple basins are generated. Additionally, this thesis contributes an open dataset with accurate ground truth for development and benchmarking 2D sonar navigation, mapping, and SLAM algorithms. Overall, this thesis demonstrates that, when the unique characteristics of affordable hard ware are considered correctly, and the methods are designed accordingly, affordable underwater robots can effectively map and explore challenging, unknown environments. The BlueAUV design, the FS2D registration method, SLAM framework for affordable hardware, and an openly available dataset provide a foundation for advancing robust mapping of challenging underwater environments within the research community.